Overview

Dataset statistics

Number of variables42
Number of observations12973
Missing cells51462
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 MiB
Average record size in memory336.0 B

Variable types

Numeric20
Text4
Categorical12
DateTime6

Alerts

id_de_parliament is highly overall correlated with elecper and 4 other fieldsHigh correlation
elecper is highly overall correlated with id_de_parliament and 4 other fieldsHigh correlation
year_birth is highly overall correlated with id_de_parliament and 3 other fieldsHigh correlation
party_elec is highly overall correlated with party_elecdet and 3 other fieldsHigh correlation
party_elecdet is highly overall correlated with party_elec and 3 other fieldsHigh correlation
districtvote is highly overall correlated with closeness_district and 2 other fieldsHigh correlation
closeness_district is highly overall correlated with districtvoteHigh correlation
list is highly overall correlated with listpos and 1 other fieldsHigh correlation
listpos is highly overall correlated with list and 1 other fieldsHigh correlation
listpos_total is highly overall correlated with list and 1 other fieldsHigh correlation
elecsafe_district is highly overall correlated with districtvote and 2 other fieldsHigh correlation
elecsafe_list is highly overall correlated with elecsafe_overall and 2 other fieldsHigh correlation
elecsafe_overall is highly overall correlated with elecsafe_listHigh correlation
partyid_cmp is highly overall correlated with partyid_parlgov and 2 other fieldsHigh correlation
partyid_ches is highly overall correlated with party_elec and 3 other fieldsHigh correlation
partyid_parlgov is highly overall correlated with partyid_cmp and 2 other fieldsHigh correlation
partyid_parlgov2 is highly overall correlated with party_elec and 4 other fieldsHigh correlation
id_de_parliament_string is highly overall correlated with id_de_parliament and 4 other fieldsHigh correlation
mp_id_old is highly overall correlated with id_de_parliament and 3 other fieldsHigh correlation
mandate is highly overall correlated with elecsafe_district and 3 other fieldsHigh correlation
mandate_detailed is highly overall correlated with elecsafe_district and 1 other fieldsHigh correlation
dualcand is highly overall correlated with elecsafe_listHigh correlation
partyid_bl is highly overall correlated with id_de_parliament and 9 other fieldsHigh correlation
office_spell is highly imbalanced (76.5%)Imbalance
minister is highly imbalanced (79.8%)Imbalance
junminister is highly imbalanced (76.9%)Imbalance
parlpres is highly imbalanced (91.9%)Imbalance
commchair is highly imbalanced (56.8%)Imbalance
ppgchair is highly imbalanced (68.2%)Imbalance
whip is highly imbalanced (81.2%)Imbalance
district_id has 1463 (11.3%) missing valuesMissing
districtvote has 6595 (50.8%) missing valuesMissing
closeness_district has 1463 (11.3%) missing valuesMissing
list has 2195 (16.9%) missing valuesMissing
listpos has 2511 (19.4%) missing valuesMissing
listpos_total has 2623 (20.2%) missing valuesMissing
elecsafe_district has 1782 (13.7%) missing valuesMissing
elecsafe_list has 1782 (13.7%) missing valuesMissing
elecsafe_overall has 1782 (13.7%) missing valuesMissing
partyid_ches has 8462 (65.2%) missing valuesMissing
partyid_bl has 12320 (95.0%) missing valuesMissing
partyid_parlgov2 has 6299 (48.6%) missing valuesMissing
mp_id_old has 1735 (13.4%) missing valuesMissing
id_de_manow has 343 (2.6%) missing valuesMissing
id_de_parliament is highly skewed (γ1 = 81.98301462)Skewed
districtvote is highly skewed (γ1 = 54.62250718)Skewed
id_de_parliament_string is highly skewed (γ1 = 81.98301462)Skewed
elecsafe_district has 807 (6.2%) zerosZeros
elecsafe_list has 1695 (13.1%) zerosZeros

Reproduction

Analysis started2023-12-03 10:21:10.384626
Analysis finished2023-12-03 10:21:58.645585
Duration48.26 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

id_de_parliament
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4098
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11009738
Minimum11000001
Maximum66666664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:21:58.880347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11000001
5-th percentile11000197
Q111000958
median11001888
Q311002808
95-th percentile11004297
Maximum66666664
Range55666663
Interquartile range (IQR)1850

Descriptive statistics

Standard deviation625963.25
Coefficient of variation (CV)0.056855415
Kurtosis6795.3146
Mean11009738
Median Absolute Deviation (MAD)926
Skewness81.983015
Sum1.4282934 × 1011
Variance3.9182999 × 1011
MonotonicityNot monotonic
2023-12-03T11:21:59.001167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001938 18
 
0.1%
11002531 17
 
0.1%
11001012 17
 
0.1%
11001512 16
 
0.1%
11000102 15
 
0.1%
11002525 14
 
0.1%
11001849 14
 
0.1%
11002270 14
 
0.1%
11000570 14
 
0.1%
11002444 13
 
0.1%
Other values (4088) 12821
98.8%
ValueCountFrequency (%)
11000001 7
0.1%
11000002 6
< 0.1%
11000003 3
< 0.1%
11000004 2
 
< 0.1%
11000005 5
< 0.1%
11000007 4
< 0.1%
11000008 1
 
< 0.1%
11000009 7
0.1%
11000010 4
< 0.1%
11000011 4
< 0.1%
ValueCountFrequency (%)
66666664 1
< 0.1%
55555556 1
< 0.1%
11004972 1
< 0.1%
11004971 1
< 0.1%
11004970 1
< 0.1%
11004969 1
< 0.1%
11004968 1
< 0.1%
11004967 1
< 0.1%
11004966 1
< 0.1%
11004962 1
< 0.1%
Distinct3432
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
2023-12-03T11:21:59.191689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length27
Mean length7.337393
Min length2

Characters and Unicode

Total characters95188
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1004 ?
Unique (%)7.7%

Sample

1st rowAbelein
2nd rowAbelein
3rd rowAbelein
4th rowAbelein
5th rowAbelein
ValueCountFrequency (%)
schmidt 135
 
1.0%
mueller 120
 
0.9%
fischer 53
 
0.4%
schroeder 40
 
0.3%
schneider 40
 
0.3%
schaefer 36
 
0.3%
vogel 34
 
0.3%
jahn 34
 
0.3%
becker 33
 
0.3%
neumann 33
 
0.3%
Other values (3437) 12567
95.7%
2023-12-03T11:21:59.506128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 14491
15.2%
r 8542
 
9.0%
n 6654
 
7.0%
a 5466
 
5.7%
i 4918
 
5.2%
l 4900
 
5.1%
h 4395
 
4.6%
t 4137
 
4.3%
s 3680
 
3.9%
c 3330
 
3.5%
Other values (47) 34675
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80265
84.3%
Uppercase Letter 13582
 
14.3%
Other Symbol 578
 
0.6%
Dash Punctuation 481
 
0.5%
Space Separator 164
 
0.2%
Close Punctuation 59
 
0.1%
Open Punctuation 59
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14491
18.1%
r 8542
10.6%
n 6654
 
8.3%
a 5466
 
6.8%
i 4918
 
6.1%
l 4900
 
6.1%
h 4395
 
5.5%
t 4137
 
5.2%
s 3680
 
4.6%
c 3330
 
4.1%
Other values (16) 19752
24.6%
Uppercase Letter
ValueCountFrequency (%)
S 2015
14.8%
B 1344
9.9%
H 1206
 
8.9%
K 1108
 
8.2%
M 959
 
7.1%
W 950
 
7.0%
L 755
 
5.6%
R 724
 
5.3%
G 720
 
5.3%
F 536
 
3.9%
Other values (14) 3265
24.0%
Close Punctuation
ValueCountFrequency (%)
) 58
98.3%
] 1
 
1.7%
Open Punctuation
ValueCountFrequency (%)
( 58
98.3%
[ 1
 
1.7%
Other Symbol
ValueCountFrequency (%)
� 578
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 481
100.0%
Space Separator
ValueCountFrequency (%)
164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93847
98.6%
Common 1341
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14491
15.4%
r 8542
 
9.1%
n 6654
 
7.1%
a 5466
 
5.8%
i 4918
 
5.2%
l 4900
 
5.2%
h 4395
 
4.7%
t 4137
 
4.4%
s 3680
 
3.9%
c 3330
 
3.5%
Other values (40) 33334
35.5%
Common
ValueCountFrequency (%)
� 578
43.1%
- 481
35.9%
164
 
12.2%
) 58
 
4.3%
( 58
 
4.3%
[ 1
 
0.1%
] 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94610
99.4%
Specials 578
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14491
15.3%
r 8542
 
9.0%
n 6654
 
7.0%
a 5466
 
5.8%
i 4918
 
5.2%
l 4900
 
5.2%
h 4395
 
4.6%
t 4137
 
4.4%
s 3680
 
3.9%
c 3330
 
3.5%
Other values (46) 34097
36.0%
Specials
ValueCountFrequency (%)
� 578
100.0%
Distinct1473
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
2023-12-03T11:21:59.715113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length37
Median length34
Mean length7.1416789
Min length3

Characters and Unicode

Total characters92649
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique491 ?
Unique (%)3.8%

Sample

1st rowManfred
2nd rowManfred
3rd rowManfred
4th rowManfred
5th rowManfred
ValueCountFrequency (%)
hans 328
 
2.3%
peter 321
 
2.2%
karl 314
 
2.2%
wolfgang 276
 
1.9%
hermann 250
 
1.7%
josef 217
 
1.5%
heinrich 210
 
1.4%
franz 188
 
1.3%
klaus 186
 
1.3%
wilhelm 185
 
1.3%
Other values (985) 12018
82.9%
2023-12-03T11:22:00.058082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9764
 
10.5%
r 9655
 
10.4%
a 8152
 
8.8%
n 6695
 
7.2%
i 6180
 
6.7%
t 5034
 
5.4%
l 4962
 
5.4%
o 3621
 
3.9%
s 3202
 
3.5%
h 3166
 
3.4%
Other values (48) 32218
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 75002
81.0%
Uppercase Letter 15154
 
16.4%
Space Separator 1527
 
1.6%
Dash Punctuation 657
 
0.7%
Other Symbol 155
 
0.2%
Other Punctuation 88
 
0.1%
Open Punctuation 33
 
< 0.1%
Close Punctuation 33
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9764
13.0%
r 9655
12.9%
a 8152
10.9%
n 6695
8.9%
i 6180
8.2%
t 5034
 
6.7%
l 4962
 
6.6%
o 3621
 
4.8%
s 3202
 
4.3%
h 3166
 
4.2%
Other values (15) 14571
19.4%
Uppercase Letter
ValueCountFrequency (%)
H 2222
14.7%
W 1149
 
7.6%
K 1091
 
7.2%
A 1047
 
6.9%
M 1027
 
6.8%
J 971
 
6.4%
G 954
 
6.3%
E 924
 
6.1%
R 775
 
5.1%
F 715
 
4.7%
Other values (15) 4279
28.2%
Other Punctuation
ValueCountFrequency (%)
. 84
95.5%
\ 2
 
2.3%
" 2
 
2.3%
Space Separator
ValueCountFrequency (%)
1527
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 657
100.0%
Other Symbol
ValueCountFrequency (%)
� 155
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90156
97.3%
Common 2493
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9764
 
10.8%
r 9655
 
10.7%
a 8152
 
9.0%
n 6695
 
7.4%
i 6180
 
6.9%
t 5034
 
5.6%
l 4962
 
5.5%
o 3621
 
4.0%
s 3202
 
3.6%
h 3166
 
3.5%
Other values (40) 29725
33.0%
Common
ValueCountFrequency (%)
1527
61.3%
- 657
26.4%
� 155
 
6.2%
. 84
 
3.4%
( 33
 
1.3%
) 33
 
1.3%
\ 2
 
0.1%
" 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92494
99.8%
Specials 155
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9764
 
10.6%
r 9655
 
10.4%
a 8152
 
8.8%
n 6695
 
7.2%
i 6180
 
6.7%
t 5034
 
5.4%
l 4962
 
5.4%
o 3621
 
3.9%
s 3202
 
3.5%
h 3166
 
3.4%
Other values (47) 32063
34.7%
Specials
ValueCountFrequency (%)
� 155
100.0%

elecper
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.560163
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:00.176637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q315
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.4863684
Coefficient of variation (CV)0.51953442
Kurtosis-1.1755436
Mean10.560163
Median Absolute Deviation (MAD)5
Skewness-0.11084286
Sum136997
Variance30.100238
MonotonicityNot monotonic
2023-12-03T11:22:00.269783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
19 933
 
7.2%
12 805
 
6.2%
18 802
 
6.2%
11 775
 
6.0%
14 768
 
5.9%
13 723
 
5.6%
16 680
 
5.2%
9 677
 
5.2%
17 674
 
5.2%
15 654
 
5.0%
Other values (9) 5482
42.3%
ValueCountFrequency (%)
1 556
4.3%
2 621
4.8%
3 596
4.6%
4 641
4.9%
5 637
4.9%
6 588
4.5%
7 614
4.7%
8 597
4.6%
9 677
5.2%
10 632
4.9%
ValueCountFrequency (%)
19 933
7.2%
18 802
6.2%
17 674
5.2%
16 680
5.2%
15 654
5.0%
14 768
5.9%
13 723
5.6%
12 805
6.2%
11 775
6.0%
10 632
4.9%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
1
10427 
0
2546 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10427
80.4%
0 2546
 
19.6%

Length

2023-12-03T11:22:00.374068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:00.482216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10427
80.4%
0 2546
 
19.6%

Most occurring characters

ValueCountFrequency (%)
1 10427
80.4%
0 2546
 
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10427
80.4%
0 2546
 
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10427
80.4%
0 2546
 
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10427
80.4%
0 2546
 
19.6%

year_birth
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1935.9913
Minimum1875
Maximum1992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:00.580874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1875
5-th percentile1897
Q11920
median1939
Q31952
95-th percentile1972
Maximum1992
Range117
Interquartile range (IQR)32

Descriptive statistics

Standard deviation22.696016
Coefficient of variation (CV)0.011723202
Kurtosis-0.64221048
Mean1935.9913
Median Absolute Deviation (MAD)16
Skewness-0.17601542
Sum25115615
Variance515.10916
MonotonicityNot monotonic
2023-12-03T11:22:00.706178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1943 331
 
2.6%
1942 283
 
2.2%
1949 270
 
2.1%
1944 265
 
2.0%
1940 256
 
2.0%
1939 255
 
2.0%
1941 247
 
1.9%
1929 242
 
1.9%
1952 232
 
1.8%
1936 219
 
1.7%
Other values (108) 10373
80.0%
ValueCountFrequency (%)
1875 1
 
< 0.1%
1876 8
0.1%
1877 1
 
< 0.1%
1878 2
 
< 0.1%
1879 6
< 0.1%
1880 8
0.1%
1881 12
0.1%
1882 3
 
< 0.1%
1883 11
0.1%
1884 14
0.1%
ValueCountFrequency (%)
1992 2
 
< 0.1%
1991 2
 
< 0.1%
1990 3
 
< 0.1%
1989 12
0.1%
1988 2
 
< 0.1%
1987 12
0.1%
1986 14
0.1%
1985 18
0.1%
1984 22
0.2%
1983 22
0.2%
Distinct3861
Distinct (%)29.8%
Missing1
Missing (%)< 0.1%
Memory size101.5 KiB
Minimum1875-12-14 00:00:00
Maximum1992-12-12 00:00:00
2023-12-03T11:22:00.834177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:00.963714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3863
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Minimum1875-12-14 00:00:00
Maximum1992-12-12 00:00:00
2023-12-03T11:22:01.098511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:01.228417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct685
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Minimum1949-09-07 00:00:00
Maximum2021-08-19 00:00:00
2023-12-03T11:22:01.366759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:01.487795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct687
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Minimum1949-09-15 00:00:00
Maximum2021-10-26 00:00:00
2023-12-03T11:22:01.602366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:01.724686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

office_spell
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
1.0
11638 
2.0
 
1155
3.0
 
161
4.0
 
16
5.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters38919
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11638
89.7%
2.0 1155
 
8.9%
3.0 161
 
1.2%
4.0 16
 
0.1%
5.0 3
 
< 0.1%

Length

2023-12-03T11:22:01.833326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:01.942364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11638
89.7%
2.0 1155
 
8.9%
3.0 161
 
1.2%
4.0 16
 
0.1%
5.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 12973
33.3%
0 12973
33.3%
1 11638
29.9%
2 1155
 
3.0%
3 161
 
0.4%
4 16
 
< 0.1%
5 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25946
66.7%
Other Punctuation 12973
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12973
50.0%
1 11638
44.9%
2 1155
 
4.5%
3 161
 
0.6%
4 16
 
0.1%
5 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 12973
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38919
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 12973
33.3%
0 12973
33.3%
1 11638
29.9%
2 1155
 
3.0%
3 161
 
0.4%
4 16
 
< 0.1%
5 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38919
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 12973
33.3%
0 12973
33.3%
1 11638
29.9%
2 1155
 
3.0%
3 161
 
0.4%
4 16
 
< 0.1%
5 3
 
< 0.1%
Distinct1363
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Minimum1949-09-07 00:00:00
Maximum2021-08-19 00:00:00
2023-12-03T11:22:02.053585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:02.173616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1361
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Minimum1949-09-12 00:00:00
Maximum2021-10-26 00:00:00
2023-12-03T11:22:02.292990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:02.547168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

party_elec
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5279427
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:02.646281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8856178
Coefficient of variation (CV)1.1414886
Kurtosis392.98655
Mean2.5279427
Median Absolute Deviation (MAD)1
Skewness13.616061
Sum32795
Variance8.3267901
MonotonicityNot monotonic
2023-12-03T11:22:02.735709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4682
36.1%
2 4542
35.0%
4 1223
 
9.4%
3 1112
 
8.6%
5 636
 
4.9%
6 437
 
3.4%
11 225
 
1.7%
19 112
 
0.9%
99 4
 
< 0.1%
ValueCountFrequency (%)
1 4682
36.1%
2 4542
35.0%
3 1112
 
8.6%
4 1223
 
9.4%
5 636
 
4.9%
6 437
 
3.4%
11 225
 
1.7%
19 112
 
0.9%
99 4
 
< 0.1%
ValueCountFrequency (%)
99 4
 
< 0.1%
19 112
 
0.9%
11 225
 
1.7%
6 437
 
3.4%
5 636
 
4.9%
4 1223
 
9.4%
3 1112
 
8.6%
2 4542
35.0%
1 4682
36.1%

party_elecdet
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5515301
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:02.831181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum99
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.3444327
Coefficient of variation (CV)1.3107557
Kurtosis429.8322
Mean2.5515301
Median Absolute Deviation (MAD)1
Skewness16.159997
Sum33101
Variance11.18523
MonotonicityNot monotonic
2023-12-03T11:22:02.930182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 4681
36.1%
2 4542
35.0%
4 1203
 
9.3%
3 1112
 
8.6%
5 644
 
5.0%
6 437
 
3.4%
19 109
 
0.8%
10 76
 
0.6%
7 38
 
0.3%
8 31
 
0.2%
Other values (9) 100
 
0.8%
ValueCountFrequency (%)
1 4681
36.1%
2 4542
35.0%
3 1112
 
8.6%
4 1203
 
9.3%
5 644
 
5.0%
6 437
 
3.4%
7 38
 
0.3%
8 31
 
0.2%
9 17
 
0.1%
10 76
 
0.6%
ValueCountFrequency (%)
99 8
 
0.1%
19 109
0.8%
18 3
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 24
 
0.2%
14 19
 
0.1%
13 20
 
0.2%
11 6
 
< 0.1%
10 76
0.6%

mandate
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
7103 
1
5870 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 7103
54.8%
1 5870
45.2%

Length

2023-12-03T11:22:03.030517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:03.130543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 7103
54.8%
1 5870
45.2%

Most occurring characters

ValueCountFrequency (%)
0 7103
54.8%
1 5870
45.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7103
54.8%
1 5870
45.2%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7103
54.8%
1 5870
45.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7103
54.8%
1 5870
45.2%

mandate_detailed
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
2
6231 
1
5856 
4
727 
3
 
145
5
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 6231
48.0%
1 5856
45.1%
4 727
 
5.6%
3 145
 
1.1%
5 14
 
0.1%

Length

2023-12-03T11:22:03.214853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:03.326746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 6231
48.0%
1 5856
45.1%
4 727
 
5.6%
3 145
 
1.1%
5 14
 
0.1%

Most occurring characters

ValueCountFrequency (%)
2 6231
48.0%
1 5856
45.1%
4 727
 
5.6%
3 145
 
1.1%
5 14
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6231
48.0%
1 5856
45.1%
4 727
 
5.6%
3 145
 
1.1%
5 14
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6231
48.0%
1 5856
45.1%
4 727
 
5.6%
3 145
 
1.1%
5 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6231
48.0%
1 5856
45.1%
4 727
 
5.6%
3 145
 
1.1%
5 14
 
0.1%

dualcand
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
1
9455 
0
3518 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 9455
72.9%
0 3518
 
27.1%

Length

2023-12-03T11:22:03.425407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:03.525514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9455
72.9%
0 3518
 
27.1%

Most occurring characters

ValueCountFrequency (%)
1 9455
72.9%
0 3518
 
27.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9455
72.9%
0 3518
 
27.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9455
72.9%
0 3518
 
27.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9455
72.9%
0 3518
 
27.1%

district_id
Real number (ℝ)

MISSING 

Distinct328
Distinct (%)2.8%
Missing1463
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean138.49557
Minimum1
Maximum328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:03.623418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q169
median136
Q3204
95-th percentile277
Maximum328
Range327
Interquartile range (IQR)135

Descriptive statistics

Standard deviation81.866602
Coefficient of variation (CV)0.59111351
Kurtosis-0.97830045
Mean138.49557
Median Absolute Deviation (MAD)67
Skewness0.15492446
Sum1594084
Variance6702.1405
MonotonicityNot monotonic
2023-12-03T11:22:03.763454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188 60
 
0.5%
69 58
 
0.4%
100 57
 
0.4%
140 57
 
0.4%
131 56
 
0.4%
97 56
 
0.4%
10 56
 
0.4%
163 55
 
0.4%
141 55
 
0.4%
78 54
 
0.4%
Other values (318) 10946
84.4%
(Missing) 1463
 
11.3%
ValueCountFrequency (%)
1 36
0.3%
2 36
0.3%
3 41
0.3%
4 40
0.3%
5 42
0.3%
6 38
0.3%
7 53
0.4%
8 40
0.3%
9 42
0.3%
10 56
0.4%
ValueCountFrequency (%)
328 10
0.1%
327 5
< 0.1%
326 5
< 0.1%
325 3
 
< 0.1%
324 6
< 0.1%
323 7
0.1%
322 4
 
< 0.1%
321 3
 
< 0.1%
320 5
< 0.1%
319 9
0.1%

districtvote
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1526
Distinct (%)23.9%
Missing6595
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean0.47831726
Minimum0.030305019
Maximum48.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:03.889391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.030305019
5-th percentile0.1693653
Q10.415
median0.478
Q30.53735728
95-th percentile0.647
Maximum48.9
Range48.869695
Interquartile range (IQR)0.12235728

Descriptive statistics

Standard deviation0.86713661
Coefficient of variation (CV)1.8128901
Kurtosis3049.332
Mean0.47831726
Median Absolute Deviation (MAD)0.061
Skewness54.622507
Sum3050.7075
Variance0.75192591
MonotonicityNot monotonic
2023-12-03T11:22:04.018608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.469 41
 
0.3%
0.488 40
 
0.3%
0.486 39
 
0.3%
0.501 39
 
0.3%
0.477 38
 
0.3%
0.483 38
 
0.3%
0.479 37
 
0.3%
0.49 37
 
0.3%
0.485 37
 
0.3%
0.474 35
 
0.3%
Other values (1516) 5997
46.2%
(Missing) 6595
50.8%
ValueCountFrequency (%)
0.03030501865 1
< 0.1%
0.03500933573 1
< 0.1%
0.03593908623 1
< 0.1%
0.03929926082 1
< 0.1%
0.04234809056 2
< 0.1%
0.04536836222 1
< 0.1%
0.04541448131 1
< 0.1%
0.04542474449 1
< 0.1%
0.04573408887 1
< 0.1%
0.04603046179 2
< 0.1%
ValueCountFrequency (%)
48.9 2
< 0.1%
0.82 1
< 0.1%
0.819 2
< 0.1%
0.802 1
< 0.1%
0.8 1
< 0.1%
0.796 2
< 0.1%
0.794 1
< 0.1%
0.791 1
< 0.1%
0.777 1
< 0.1%
0.772 2
< 0.1%

closeness_district
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5388
Distinct (%)46.8%
Missing1463
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean0.15733776
Minimum0
Maximum0.74558337
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:04.144067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.011869436
Q10.061072528
median0.12996608
Q30.23175901
95-th percentile0.39154502
Maximum0.74558337
Range0.74558337
Interquartile range (IQR)0.17068648

Descriptive statistics

Standard deviation0.12078013
Coefficient of variation (CV)0.76764876
Kurtosis0.54200946
Mean0.15733776
Median Absolute Deviation (MAD)0.079022462
Skewness0.94542883
Sum1810.9576
Variance0.014587841
MonotonicityNot monotonic
2023-12-03T11:22:04.272103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05799999833 13
 
0.1%
0.201000005 12
 
0.1%
0.2649999857 11
 
0.1%
0.04399999976 10
 
0.1%
0.08600000292 10
 
0.1%
0.09300000221 10
 
0.1%
0.003000000026 9
 
0.1%
0.1770000011 9
 
0.1%
0.04100000113 9
 
0.1%
0.1469999999 8
 
0.1%
Other values (5378) 11409
87.9%
(Missing) 1463
 
11.3%
ValueCountFrequency (%)
0 3
< 0.1%
0.0001193295515 2
< 0.1%
0.0001274707415 2
< 0.1%
0.0001554085152 4
< 0.1%
0.0002140987479 1
 
< 0.1%
0.0002601637297 2
< 0.1%
0.0002954295314 3
< 0.1%
0.0003683995088 2
< 0.1%
0.0004321332064 3
< 0.1%
0.0004407349255 2
< 0.1%
ValueCountFrequency (%)
0.7455833727 1
< 0.1%
0.7098927795 1
< 0.1%
0.703929966 2
< 0.1%
0.6995185003 1
< 0.1%
0.6926352573 1
< 0.1%
0.6925889467 1
< 0.1%
0.6844805195 2
< 0.1%
0.6800405336 1
< 0.1%
0.6753564507 1
< 0.1%
0.6523922627 2
< 0.1%

list
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)0.2%
Missing2195
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean4.7746335
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:04.401090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile13
Maximum19
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.953187
Coefficient of variation (CV)0.8279561
Kurtosis0.59744481
Mean4.7746335
Median Absolute Deviation (MAD)3
Skewness1.1666022
Sum51461
Variance15.627687
MonotonicityNot monotonic
2023-12-03T11:22:04.493332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 2641
20.4%
2 1448
11.2%
4 1292
10.0%
3 1103
8.5%
5 1027
 
7.9%
7 606
 
4.7%
8 514
 
4.0%
9 481
 
3.7%
13 335
 
2.6%
6 271
 
2.1%
Other values (9) 1060
8.2%
(Missing) 2195
16.9%
ValueCountFrequency (%)
1 2641
20.4%
2 1448
11.2%
3 1103
8.5%
4 1292
10.0%
5 1027
 
7.9%
6 271
 
2.1%
7 606
 
4.7%
8 514
 
4.0%
9 481
 
3.7%
10 198
 
1.5%
ValueCountFrequency (%)
19 7
 
0.1%
18 29
 
0.2%
17 6
 
< 0.1%
16 120
 
0.9%
15 182
1.4%
14 124
 
1.0%
13 335
2.6%
12 191
1.5%
11 203
1.6%
10 198
1.5%

listpos
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)0.9%
Missing2511
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean12.20866
Minimum1
Maximum94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:04.615646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q316
95-th percentile42
Maximum94
Range93
Interquartile range (IQR)13

Descriptive statistics

Standard deviation13.629584
Coefficient of variation (CV)1.1163866
Kurtosis5.5459727
Mean12.20866
Median Absolute Deviation (MAD)5
Skewness2.1875291
Sum127727
Variance185.76556
MonotonicityNot monotonic
2023-12-03T11:22:04.737650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1103
 
8.5%
2 969
 
7.5%
3 849
 
6.5%
4 734
 
5.7%
5 636
 
4.9%
6 548
 
4.2%
7 470
 
3.6%
8 420
 
3.2%
9 382
 
2.9%
10 341
 
2.6%
Other values (79) 4010
30.9%
(Missing) 2511
19.4%
ValueCountFrequency (%)
1 1103
8.5%
2 969
7.5%
3 849
6.5%
4 734
5.7%
5 636
4.9%
6 548
4.2%
7 470
3.6%
8 420
 
3.2%
9 382
 
2.9%
10 341
 
2.6%
ValueCountFrequency (%)
94 1
 
< 0.1%
89 2
< 0.1%
88 2
< 0.1%
87 2
< 0.1%
86 3
< 0.1%
85 1
 
< 0.1%
84 2
< 0.1%
83 1
 
< 0.1%
81 4
< 0.1%
80 4
< 0.1%

listpos_total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct107
Distinct (%)1.0%
Missing2623
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean51.234879
Minimum3
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:04.860621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile11
Q128
median47
Q370
95-th percentile115
Maximum152
Range149
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.36002
Coefficient of variation (CV)0.59256546
Kurtosis0.34193372
Mean51.234879
Median Absolute Deviation (MAD)21
Skewness0.7905838
Sum530281
Variance921.7308
MonotonicityNot monotonic
2023-12-03T11:22:04.987209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 314
 
2.4%
89 296
 
2.3%
80 250
 
1.9%
63 248
 
1.9%
40 242
 
1.9%
36 240
 
1.8%
60 222
 
1.7%
30 209
 
1.6%
12 204
 
1.6%
25 201
 
1.5%
Other values (97) 7924
61.1%
(Missing) 2623
 
20.2%
ValueCountFrequency (%)
3 2
 
< 0.1%
4 11
 
0.1%
5 40
 
0.3%
6 79
 
0.6%
7 64
 
0.5%
8 54
 
0.4%
9 58
 
0.4%
10 175
1.3%
11 84
0.6%
12 204
1.6%
ValueCountFrequency (%)
152 47
0.4%
151 47
0.4%
136 30
0.2%
124 34
0.3%
123 66
0.5%
122 28
0.2%
121 35
0.3%
120 45
0.3%
118 51
0.4%
117 51
0.4%

elecsafe_district
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9278
Distinct (%)82.9%
Missing1782
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.43916823
Minimum0
Maximum1
Zeros807
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:05.123705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0039665741
median0.35896128
Q30.91455752
95-th percentile0.99934029
Maximum1
Range1
Interquartile range (IQR)0.91059095

Descriptive statistics

Standard deviation0.41475403
Coefficient of variation (CV)0.9444081
Kurtosis-1.6963852
Mean0.43916823
Median Absolute Deviation (MAD)0.35881036
Skewness0.2020888
Sum4914.7316
Variance0.17202091
MonotonicityNot monotonic
2023-12-03T11:22:05.255750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 807
 
6.2%
1 8
 
0.1%
0.003976718 5
 
< 0.1%
0.053469744 5
 
< 0.1%
0.9727668 5
 
< 0.1%
0.99808747 4
 
< 0.1%
0.0021558153 4
 
< 0.1%
0.9959763 4
 
< 0.1%
0.028821371 4
 
< 0.1%
0.9615659 4
 
< 0.1%
Other values (9268) 10341
79.7%
(Missing) 1782
 
13.7%
ValueCountFrequency (%)
0 807
6.2%
6.104203 × 10-141
 
< 0.1%
3.0204227 × 10-131
 
< 0.1%
1.4252041 × 10-121
 
< 0.1%
3.2304855 × 10-121
 
< 0.1%
4.1129405 × 10-121
 
< 0.1%
4.757858 × 10-121
 
< 0.1%
9.232058 × 10-121
 
< 0.1%
9.885444 × 10-121
 
< 0.1%
1.0692277 × 10-111
 
< 0.1%
ValueCountFrequency (%)
1 8
0.1%
0.99999994 3
 
< 0.1%
0.9999999 2
 
< 0.1%
0.9999998 1
 
< 0.1%
0.99999976 1
 
< 0.1%
0.9999997 1
 
< 0.1%
0.99999964 1
 
< 0.1%
0.9999996 2
 
< 0.1%
0.9999995 1
 
< 0.1%
0.999999 1
 
< 0.1%

elecsafe_list
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7166
Distinct (%)64.0%
Missing1782
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.59242183
Minimum0
Maximum1
Zeros1695
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:05.386714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.044417254
median0.8011186
Q30.99900235
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.9545851

Descriptive statistics

Standard deviation0.42692508
Coefficient of variation (CV)0.72064374
Kurtosis-1.6267218
Mean0.59242183
Median Absolute Deviation (MAD)0.1988814
Skewness-0.39255356
Sum6629.7927
Variance0.18226503
MonotonicityNot monotonic
2023-12-03T11:22:05.511593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1695
 
13.1%
1 844
 
6.5%
0.99999994 123
 
0.9%
0.9999999 63
 
0.5%
0.99999976 31
 
0.2%
0.9999997 29
 
0.2%
0.9999998 27
 
0.2%
0.9999996 19
 
0.1%
0.99999934 18
 
0.1%
0.99999946 16
 
0.1%
Other values (7156) 8326
64.2%
(Missing) 1782
 
13.7%
ValueCountFrequency (%)
0 1695
13.1%
2.8512329 × 10-221
 
< 0.1%
1.8335192 × 10-201
 
< 0.1%
2.2296522 × 10-191
 
< 0.1%
5.127288 × 10-191
 
< 0.1%
6.31492 × 10-191
 
< 0.1%
6.2350418 × 10-181
 
< 0.1%
1.3826948 × 10-171
 
< 0.1%
2.558272 × 10-171
 
< 0.1%
6.395317 × 10-171
 
< 0.1%
ValueCountFrequency (%)
1 844
6.5%
0.99999994 123
 
0.9%
0.9999999 63
 
0.5%
0.9999998 27
 
0.2%
0.99999976 31
 
0.2%
0.9999997 29
 
0.2%
0.99999964 15
 
0.1%
0.9999996 19
 
0.1%
0.9999995 11
 
0.1%
0.99999946 16
 
0.1%

elecsafe_overall
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8421
Distinct (%)75.2%
Missing1782
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean0.85927793
Minimum1.0863167 × 10-12
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:05.638589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0863167 × 10-12
5-th percentile0.24567127
Q10.82629905
median0.9872684
Q30.9998078
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.17350875

Descriptive statistics

Standard deviation0.24006956
Coefficient of variation (CV)0.27938523
Kurtosis3.0943745
Mean0.85927793
Median Absolute Deviation (MAD)0.0127316
Skewness-1.9851637
Sum9616.1793
Variance0.057633396
MonotonicityNot monotonic
2023-12-03T11:22:05.767692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 936
 
7.2%
0.99999994 150
 
1.2%
0.9999999 71
 
0.5%
0.99999976 34
 
0.3%
0.9999998 32
 
0.2%
0.9999997 30
 
0.2%
0.9999996 25
 
0.2%
0.99999964 19
 
0.1%
0.9999989 17
 
0.1%
0.9999993 16
 
0.1%
Other values (8411) 9861
76.0%
(Missing) 1782
 
13.7%
ValueCountFrequency (%)
1.0863167 × 10-121
< 0.1%
2.639847 × 10-101
< 0.1%
8.444629 × 10-101
< 0.1%
1.027684 × 10-91
< 0.1%
1.3739001 × 10-91
< 0.1%
3.4957903 × 10-91
< 0.1%
1.4471388 × 10-81
< 0.1%
2.6144459 × 10-81
< 0.1%
1.2395564 × 10-71
< 0.1%
1.8579316 × 10-71
< 0.1%
ValueCountFrequency (%)
1 936
7.2%
0.99999994 150
 
1.2%
0.9999999 71
 
0.5%
0.9999998 32
 
0.2%
0.99999976 34
 
0.3%
0.9999997 30
 
0.2%
0.99999964 19
 
0.1%
0.9999996 25
 
0.2%
0.9999995 16
 
0.1%
0.99999946 16
 
0.1%

minister
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
12565 
1
 
408

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12565
96.9%
1 408
 
3.1%

Length

2023-12-03T11:22:06.023029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:06.121186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12565
96.9%
1 408
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 12565
96.9%
1 408
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12565
96.9%
1 408
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12565
96.9%
1 408
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12565
96.9%
1 408
 
3.1%

junminister
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
12486 
1
 
487

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12486
96.2%
1 487
 
3.8%

Length

2023-12-03T11:22:06.204440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:06.306174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12486
96.2%
1 487
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 12486
96.2%
1 487
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12486
96.2%
1 487
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12486
96.2%
1 487
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12486
96.2%
1 487
 
3.8%

parlpres
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
12843 
1
 
130

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12843
99.0%
1 130
 
1.0%

Length

2023-12-03T11:22:06.390182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:06.492940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12843
99.0%
1 130
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 12843
99.0%
1 130
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12843
99.0%
1 130
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12843
99.0%
1 130
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12843
99.0%
1 130
 
1.0%

commchair
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
11822 
1
 
1151

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11822
91.1%
1 1151
 
8.9%

Length

2023-12-03T11:22:06.578336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:06.678394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11822
91.1%
1 1151
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 11822
91.1%
1 1151
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11822
91.1%
1 1151
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11822
91.1%
1 1151
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11822
91.1%
1 1151
 
8.9%

ppgchair
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
12225 
1
 
748

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12225
94.2%
1 748
 
5.8%

Length

2023-12-03T11:22:06.761903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:06.861899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12225
94.2%
1 748
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 12225
94.2%
1 748
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12225
94.2%
1 748
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12225
94.2%
1 748
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12225
94.2%
1 748
 
5.8%

whip
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
0
12599 
1
 
374

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12973
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12599
97.1%
1 374
 
2.9%

Length

2023-12-03T11:22:06.943345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:07.045949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12599
97.1%
1 374
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 12599
97.1%
1 374
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12973
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12599
97.1%
1 374
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 12973
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12599
97.1%
1 374
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12599
97.1%
1 374
 
2.9%

partyid_cmp
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.1%
Missing33
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean41415.326
Minimum41111
Maximum41953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:07.127016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum41111
5-th percentile41220
Q141320
median41420
Q341521
95-th percentile41521
Maximum41953
Range842
Interquartile range (IQR)201

Descriptive statistics

Standard deviation135.56217
Coefficient of variation (CV)0.0032732368
Kurtosis2.0056342
Mean41415.326
Median Absolute Deviation (MAD)101
Skewness0.31235453
Sum5.3591432 × 108
Variance18377.101
MonotonicityNot monotonic
2023-12-03T11:22:07.216230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
41521 5654
43.6%
41320 4682
36.1%
41420 1220
 
9.4%
41113 469
 
3.6%
41222 221
 
1.7%
41111 164
 
1.3%
41221 112
 
0.9%
41953 112
 
0.9%
41223 80
 
0.6%
41620 76
 
0.6%
Other values (8) 150
 
1.2%
ValueCountFrequency (%)
41111 164
 
1.3%
41112 11
 
0.1%
41113 469
 
3.6%
41220 20
 
0.2%
41221 112
 
0.9%
41222 221
 
1.7%
41223 80
 
0.6%
41320 4682
36.1%
41420 1220
 
9.4%
41521 5654
43.6%
ValueCountFrequency (%)
41953 112
 
0.9%
41951 38
 
0.3%
41912 1
 
< 0.1%
41911 31
 
0.2%
41712 6
 
< 0.1%
41711 19
 
0.1%
41620 76
 
0.6%
41522 24
 
0.2%
41521 5654
43.6%
41420 1220
 
9.4%

partyid_ches
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.2%
Missing8462
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean302.9335
Minimum301
Maximum310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:07.309009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile301
Q1301
median302
Q3304
95-th percentile308
Maximum310
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3605239
Coefficient of variation (CV)0.0077922181
Kurtosis1.0486841
Mean302.9335
Median Absolute Deviation (MAD)1
Skewness1.4316355
Sum1366533
Variance5.5720729
MonotonicityNot monotonic
2023-12-03T11:22:07.394880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
302 1473
 
11.4%
301 1430
 
11.0%
304 419
 
3.2%
303 371
 
2.9%
308 356
 
2.7%
306 350
 
2.7%
310 112
 
0.9%
(Missing) 8462
65.2%
ValueCountFrequency (%)
301 1430
11.0%
302 1473
11.4%
303 371
 
2.9%
304 419
 
3.2%
306 350
 
2.7%
308 356
 
2.7%
310 112
 
0.9%
ValueCountFrequency (%)
310 112
 
0.9%
308 356
 
2.7%
306 350
 
2.7%
304 419
 
3.2%
303 371
 
2.9%
302 1473
11.4%
301 1430
11.0%

partyid_bl
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.8%
Missing12320
Missing (%)95.0%
Memory size101.5 KiB
307.0
266 
44.0
266 
135.0
61 
122.0
58 
249.0
 
2

Length

Max length5
Median length5
Mean length4.5926493
Min length4

Characters and Unicode

Total characters2999
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row307.0
2nd row307.0
3rd row307.0
4th row44.0
5th row307.0

Common Values

ValueCountFrequency (%)
307.0 266
 
2.1%
44.0 266
 
2.1%
135.0 61
 
0.5%
122.0 58
 
0.4%
249.0 2
 
< 0.1%
(Missing) 12320
95.0%

Length

2023-12-03T11:22:07.491759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:07.603789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
307.0 266
40.7%
44.0 266
40.7%
135.0 61
 
9.3%
122.0 58
 
8.9%
249.0 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 919
30.6%
. 653
21.8%
4 534
17.8%
3 327
 
10.9%
7 266
 
8.9%
1 119
 
4.0%
2 118
 
3.9%
5 61
 
2.0%
9 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2346
78.2%
Other Punctuation 653
 
21.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 919
39.2%
4 534
22.8%
3 327
 
13.9%
7 266
 
11.3%
1 119
 
5.1%
2 118
 
5.0%
5 61
 
2.6%
9 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 919
30.6%
. 653
21.8%
4 534
17.8%
3 327
 
10.9%
7 266
 
8.9%
1 119
 
4.0%
2 118
 
3.9%
5 61
 
2.0%
9 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 919
30.6%
. 653
21.8%
4 534
17.8%
3 327
 
10.9%
7 266
 
8.9%
1 119
 
4.0%
2 118
 
3.9%
5 61
 
2.0%
9 2
 
0.1%

partyid_parlgov
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1106.2759
Minimum137
Maximum2253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:07.697662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum137
5-th percentile543
Q1558
median772
Q31727
95-th percentile1727
Maximum2253
Range2116
Interquartile range (IQR)1169

Descriptive statistics

Standard deviation574.5149
Coefficient of variation (CV)0.51932336
Kurtosis-1.8537446
Mean1106.2759
Median Absolute Deviation (MAD)229
Skewness0.19031619
Sum14340654
Variance330067.37
MonotonicityNot monotonic
2023-12-03T11:22:07.782644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1727 5654
43.6%
558 4682
36.1%
543 1220
 
9.4%
772 644
 
5.0%
791 437
 
3.4%
2253 112
 
0.9%
912 76
 
0.6%
1507 38
 
0.3%
1131 31
 
0.2%
137 24
 
0.2%
Other values (3) 45
 
0.3%
ValueCountFrequency (%)
137 24
 
0.2%
187 6
 
< 0.1%
543 1220
 
9.4%
558 4682
36.1%
649 20
 
0.2%
772 644
 
5.0%
791 437
 
3.4%
912 76
 
0.6%
1131 31
 
0.2%
1420 19
 
0.1%
ValueCountFrequency (%)
2253 112
 
0.9%
1727 5654
43.6%
1507 38
 
0.3%
1420 19
 
0.1%
1131 31
 
0.2%
912 76
 
0.6%
791 437
 
3.4%
772 644
 
5.0%
649 20
 
0.2%
558 4682
36.1%

partyid_parlgov2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.1%
Missing6299
Missing (%)48.6%
Infinite0
Infinite (%)0.0%
Mean870.17651
Minimum358
Maximum2253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:07.869116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum358
5-th percentile558
Q1808
median808
Q3808
95-th percentile1180
Maximum2253
Range1895
Interquartile range (IQR)0

Descriptive statistics

Standard deviation245.48076
Coefficient of variation (CV)0.28210457
Kurtosis14.621432
Mean870.17651
Median Absolute Deviation (MAD)0
Skewness3.1188733
Sum5807558
Variance60260.806
MonotonicityNot monotonic
2023-12-03T11:22:07.957552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
808 4542
35.0%
1180 1112
 
8.6%
558 477
 
3.7%
772 160
 
1.2%
791 159
 
1.2%
2253 112
 
0.9%
543 95
 
0.7%
358 17
 
0.1%
(Missing) 6299
48.6%
ValueCountFrequency (%)
358 17
 
0.1%
543 95
 
0.7%
558 477
 
3.7%
772 160
 
1.2%
791 159
 
1.2%
808 4542
35.0%
1180 1112
 
8.6%
2253 112
 
0.9%
ValueCountFrequency (%)
2253 112
 
0.9%
1180 1112
 
8.6%
808 4542
35.0%
791 159
 
1.2%
772 160
 
1.2%
558 477
 
3.7%
543 95
 
0.7%
358 17
 
0.1%

id_de_parliament_string
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4098
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11009738
Minimum11000001
Maximum66666664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:08.075552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11000001
5-th percentile11000197
Q111000958
median11001888
Q311002808
95-th percentile11004297
Maximum66666664
Range55666663
Interquartile range (IQR)1850

Descriptive statistics

Standard deviation625963.25
Coefficient of variation (CV)0.056855415
Kurtosis6795.3146
Mean11009738
Median Absolute Deviation (MAD)926
Skewness81.983015
Sum1.4282934 × 1011
Variance3.9182999 × 1011
MonotonicityNot monotonic
2023-12-03T11:22:08.196553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11001938 18
 
0.1%
11002531 17
 
0.1%
11001012 17
 
0.1%
11001512 16
 
0.1%
11000102 15
 
0.1%
11002525 14
 
0.1%
11001849 14
 
0.1%
11002270 14
 
0.1%
11000570 14
 
0.1%
11002444 13
 
0.1%
Other values (4088) 12821
98.8%
ValueCountFrequency (%)
11000001 7
0.1%
11000002 6
< 0.1%
11000003 3
< 0.1%
11000004 2
 
< 0.1%
11000005 5
< 0.1%
11000007 4
< 0.1%
11000008 1
 
< 0.1%
11000009 7
0.1%
11000010 4
< 0.1%
11000011 4
< 0.1%
ValueCountFrequency (%)
66666664 1
< 0.1%
55555556 1
< 0.1%
11004972 1
< 0.1%
11004971 1
< 0.1%
11004970 1
< 0.1%
11004969 1
< 0.1%
11004968 1
< 0.1%
11004967 1
< 0.1%
11004966 1
< 0.1%
11004962 1
< 0.1%

mp_id_old
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3588
Distinct (%)31.9%
Missing1735
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean66605.833
Minimum10
Maximum182832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size101.5 KiB
2023-12-03T11:22:08.324870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile1143
Q16171
median71153.5
Q3121739
95-th percentile162679.3
Maximum182832
Range182822
Interquartile range (IQR)115568

Descriptive statistics

Standard deviation60465.146
Coefficient of variation (CV)0.90780557
Kurtosis-1.4593544
Mean66605.833
Median Absolute Deviation (MAD)62589.5
Skewness0.25193154
Sum7.4851635 × 108
Variance3.6560339 × 109
MonotonicityIncreasing
2023-12-03T11:22:08.451627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307 17
 
0.1%
10756 17
 
0.1%
6475 16
 
0.1%
70012 15
 
0.1%
397 15
 
0.1%
9654 14
 
0.1%
2414 14
 
0.1%
10700 14
 
0.1%
9703 13
 
0.1%
7880 13
 
0.1%
Other values (3578) 11090
85.5%
(Missing) 1735
 
13.4%
ValueCountFrequency (%)
10 7
0.1%
14 6
< 0.1%
19 3
< 0.1%
25 4
< 0.1%
26 1
 
< 0.1%
30 7
0.1%
34 4
< 0.1%
37 1
 
< 0.1%
46 1
 
< 0.1%
51 1
 
< 0.1%
ValueCountFrequency (%)
182832 1
 
< 0.1%
182828 1
 
< 0.1%
182803 1
 
< 0.1%
182787 1
 
< 0.1%
182781 3
< 0.1%
182773 3
< 0.1%
182771 1
 
< 0.1%
182734 4
< 0.1%
182733 1
 
< 0.1%
182686 2
< 0.1%
Distinct4071
Distinct (%)31.5%
Missing63
Missing (%)0.5%
Memory size101.5 KiB
2023-12-03T11:22:08.619005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length30
Mean length21.939814
Min length16

Characters and Unicode

Total characters283243
Distinct characters63
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1252 ?
Unique (%)9.7%

Sample

1st rowDE_Abelein_Manfred_1930
2nd rowDE_Abelein_Manfred_1930
3rd rowDE_Abelein_Manfred_1930
4th rowDE_Abelein_Manfred_1930
5th rowDE_Abelein_Manfred_1930
ValueCountFrequency (%)
de_schaeuble_wolfgang_1942 18
 
0.1%
de_wischnewski_hans_1922 17
 
0.1%
de_jahn_gerhard_1927 17
 
0.1%
de_mischnick_wolfgang_1921 16
 
0.1%
de_barzel_rainer_1924 15
 
0.1%
de_windelen_heinrich_1921 14
 
0.1%
de_riesenhuber_heinz_1935 14
 
0.1%
de_franke_egon_1913 14
 
0.1%
de_strauss_franz_1915 14
 
0.1%
de_wehner_herbert_1906 13
 
0.1%
Other values (4061) 12758
98.8%
2023-12-03T11:22:08.908004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 38730
 
13.7%
e 22255
 
7.9%
r 16450
 
5.8%
1 15366
 
5.4%
9 14162
 
5.0%
E 14083
 
5.0%
D 13650
 
4.8%
a 12462
 
4.4%
n 12212
 
4.3%
i 9768
 
3.4%
Other values (53) 114105
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141210
49.9%
Decimal Number 51651
 
18.2%
Uppercase Letter 51641
 
18.2%
Connector Punctuation 38730
 
13.7%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22255
15.8%
r 16450
11.6%
a 12462
 
8.8%
n 12212
 
8.6%
i 9768
 
6.9%
l 8938
 
6.3%
t 8298
 
5.9%
h 6748
 
4.8%
s 6388
 
4.5%
o 6117
 
4.3%
Other values (16) 31574
22.4%
Uppercase Letter
ValueCountFrequency (%)
E 14083
27.3%
D 13650
26.4%
H 3119
 
6.0%
S 2268
 
4.4%
K 2068
 
4.0%
W 1855
 
3.6%
M 1789
 
3.5%
B 1665
 
3.2%
G 1463
 
2.8%
R 1416
 
2.7%
Other values (15) 8265
16.0%
Decimal Number
ValueCountFrequency (%)
1 15366
29.7%
9 14162
27.4%
4 3799
 
7.4%
3 3114
 
6.0%
2 3062
 
5.9%
5 3055
 
5.9%
8 2417
 
4.7%
0 2416
 
4.7%
6 2356
 
4.6%
7 1904
 
3.7%
Connector Punctuation
ValueCountFrequency (%)
_ 38730
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 192851
68.1%
Common 90392
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22255
 
11.5%
r 16450
 
8.5%
E 14083
 
7.3%
D 13650
 
7.1%
a 12462
 
6.5%
n 12212
 
6.3%
i 9768
 
5.1%
l 8938
 
4.6%
t 8298
 
4.3%
h 6748
 
3.5%
Other values (41) 67987
35.3%
Common
ValueCountFrequency (%)
_ 38730
42.8%
1 15366
 
17.0%
9 14162
 
15.7%
4 3799
 
4.2%
3 3114
 
3.4%
2 3062
 
3.4%
5 3055
 
3.4%
8 2417
 
2.7%
0 2416
 
2.7%
6 2356
 
2.6%
Other values (2) 1915
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 38730
 
13.7%
e 22255
 
7.9%
r 16450
 
5.8%
1 15366
 
5.4%
9 14162
 
5.0%
E 14083
 
5.0%
D 13650
 
4.8%
a 12462
 
4.4%
n 12212
 
4.3%
i 9768
 
3.4%
Other values (53) 114105
40.3%

id_de_manow
Text

MISSING 

Distinct3823
Distinct (%)30.3%
Missing343
Missing (%)2.6%
Memory size101.5 KiB
2023-12-03T11:22:09.158005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.0754553
Min length2

Characters and Unicode

Total characters64103
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1032 ?
Unique (%)8.2%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10
ValueCountFrequency (%)
70012 18
 
0.1%
4307 17
 
0.1%
10756 17
 
0.1%
6475 16
 
0.1%
397 15
 
0.1%
10700 14
 
0.1%
7880 14
 
0.1%
2414 14
 
0.1%
9654 14
 
0.1%
70397 13
 
0.1%
Other values (3813) 12478
98.8%
2023-12-03T11:22:09.529592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13931
21.7%
2 7338
11.4%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64030
99.9%
Other Punctuation 73
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13931
21.8%
2 7338
11.5%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%
Other Punctuation
ValueCountFrequency (%)
; 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64103
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13931
21.7%
2 7338
11.4%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13931
21.7%
2 7338
11.4%
0 7231
11.3%
3 5810
9.1%
7 5568
 
8.7%
8 5238
 
8.2%
6 4812
 
7.5%
4 4777
 
7.5%
9 4689
 
7.3%
5 4636
 
7.2%

Interactions

2023-12-03T11:21:55.091019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:14.744152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.778623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.937653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.952103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.970717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:25.092617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:27.135085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:29.119200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:31.370555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:33.456340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:35.507196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.880786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:40.077930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:42.325599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:44.402715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:46.366245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:48.510224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:50.520202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:52.824036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:55.195096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:14.853215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.879189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:19.040655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:21.052101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-12-03T11:21:26.216906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.229894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:30.427122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:32.511384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:34.578558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:36.853752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.098898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:41.234567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:43.480295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:45.501561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:47.471137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:49.596155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:51.735625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.127130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:56.394572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:15.981786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.012169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.158174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.165459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.302651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:26.318866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.327330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:30.531175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:32.625383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:34.700532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:36.968464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.199902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:41.337593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:43.580366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:45.597139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:47.569134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:49.700143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:51.852623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.246132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:56.502572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.082935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.116202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.258174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.267459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.403659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:26.419074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.426301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:30.634158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:32.727539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:34.798530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.079464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.311902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:41.436564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:43.681367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:45.695140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:47.797178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:49.805125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:51.972622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.375131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:56.607569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.182950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.220199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.359204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.370460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.501659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:26.519790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.524329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:30.740312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:32.834538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:34.896531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.187792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.416900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:41.542561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:43.780845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:45.792109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:47.893134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:49.909154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:52.087143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.482318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:56.706569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.273846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.312872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.449238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.463489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.591499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:26.612761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.613335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:30.835710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:32.930538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:34.994151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.286787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.526900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:41.635590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:43.872850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:45.879112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:47.983648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:49.998205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:52.192142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.577345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:56.833572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.372876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.407872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.546209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.561488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.687966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:26.710761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.707299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:30.934710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:33.033393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:35.097151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.398784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.629900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:41.896288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:43.972846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:45.971496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:48.073730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:50.099206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:52.332145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.677343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:56.954079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.463877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.503856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.639210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.654952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.779966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:26.801762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.798937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:31.032799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:33.128390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:35.193151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.530815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.745900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:42.006717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:44.084424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:46.058809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:48.179713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:50.188205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:52.471343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.772270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:57.056089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.571875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.722847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.745210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.762707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.886633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:26.911957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:28.905203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:31.147827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:33.241617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:35.302151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.638785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.860905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:42.114629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:44.191426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:46.166808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:48.298701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:50.298203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:52.593946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.878259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:57.162049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:16.671875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:18.828872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:20.847120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:22.865733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:24.986644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:27.017056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:29.009947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:31.255803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:33.346341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:35.400197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:37.756791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:39.968899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:42.217600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:44.293050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:46.263781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:48.398199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:50.400206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:52.710363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:21:54.979487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-12-03T11:22:09.696568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
id_de_parliamentelecperyear_birthparty_elecparty_elecdetdistrict_iddistrictvotecloseness_districtlistlistposlistpos_totalelecsafe_districtelecsafe_listelecsafe_overallpartyid_cmppartyid_chespartyid_parlgovpartyid_parlgov2id_de_parliament_stringmp_id_oldgenderoffice_spellmandatemandate_detaileddualcandministerjunministerparlprescommchairppgchairwhippartyid_bl
id_de_parliament1.0000.6420.6230.1150.1140.077-0.416-0.0130.065-0.054-0.180-0.060-0.146-0.258-0.1120.1260.024-0.2761.0000.6200.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0001.000
elecper0.6421.0000.9210.1020.1010.140-0.414-0.0210.083-0.112-0.204-0.001-0.119-0.185-0.1290.0770.054-0.3020.6420.7590.2890.0690.0560.2200.2720.0510.1000.0000.0860.0400.0321.000
year_birth0.6230.9211.0000.1160.1150.158-0.385-0.0190.092-0.066-0.230-0.039-0.154-0.243-0.1580.0990.034-0.2700.6230.7590.3160.0230.0520.0720.2370.0700.1030.0630.1120.0320.0600.087
party_elec0.1150.1020.1161.0000.9990.1430.0020.1030.015-0.334-0.330-0.223-0.131-0.2120.3390.5280.3030.5410.1150.0650.0420.0430.0960.0700.0260.0000.0300.0000.0000.0450.0271.000
party_elecdet0.1140.1010.1150.9991.0000.1430.0020.1030.016-0.333-0.330-0.223-0.131-0.2120.3400.5280.3020.5390.1140.0640.0260.0360.1030.0730.0250.0180.0210.0000.0000.0110.0141.000
district_id0.0770.1400.1580.1430.1431.0000.0290.222-0.058-0.035-0.0520.034-0.0710.0690.0470.1520.0820.3010.0770.1410.0750.0000.0080.0140.1800.0360.0220.0300.0320.0240.0330.031
districtvote-0.416-0.414-0.3850.0020.0020.0291.0000.571-0.1860.1930.3770.771-0.3070.4870.183-0.1220.1750.474-0.416-0.2360.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
closeness_district-0.013-0.021-0.0190.1030.1030.2220.5711.000-0.1600.0260.0340.105-0.1660.1930.0870.2290.0990.205-0.013-0.1060.0510.0000.0620.0360.2750.0450.0040.0000.0410.0000.0000.000
list0.0650.0830.0920.0150.016-0.058-0.186-0.1601.000-0.513-0.7180.133-0.224-0.259-0.026-0.0700.043-0.1410.0650.1970.0540.0000.1200.0740.2240.0400.0160.0060.0350.0230.0600.000
listpos-0.054-0.112-0.066-0.334-0.333-0.0350.1930.026-0.5131.0000.5820.108-0.278-0.1580.074-0.2100.0740.175-0.054-0.0200.1240.0580.2230.1660.1150.1230.0610.0690.0420.1490.0790.172
listpos_total-0.180-0.204-0.230-0.330-0.330-0.0520.3770.034-0.7180.5821.0000.1210.2310.3370.155-0.2060.0370.235-0.180-0.2620.1450.0190.1660.0980.1560.0700.0510.0370.0210.0410.0460.306
elecsafe_district-0.060-0.001-0.039-0.223-0.2230.0340.7710.1050.1330.1080.1211.000-0.4240.2480.283-0.3350.3030.338-0.060-0.0310.2090.0140.9850.6980.3580.0580.0540.0230.0300.0140.0480.317
elecsafe_list-0.146-0.119-0.154-0.131-0.131-0.071-0.307-0.166-0.224-0.2780.231-0.4241.0000.632-0.2000.058-0.243-0.196-0.146-0.1600.2060.0310.5370.4500.5580.1110.0720.0610.0530.0890.0440.102
elecsafe_overall-0.258-0.185-0.243-0.212-0.2120.0690.4870.193-0.259-0.1580.3370.2480.6321.0000.061-0.1020.0090.173-0.258-0.2530.0900.0330.2930.4960.1420.0950.0900.0540.0850.0700.0160.204
partyid_cmp-0.112-0.129-0.1580.3390.3400.0470.1830.087-0.0260.0740.1550.283-0.2000.0611.000-0.4530.7180.751-0.112-0.1680.2720.0370.4280.2200.3630.0810.0480.0270.0190.1270.1151.000
partyid_ches0.1260.0770.0990.5280.5280.152-0.1220.229-0.070-0.210-0.206-0.3350.058-0.102-0.4531.000-0.2610.0380.1260.0820.2700.0250.5560.3980.3970.0600.0830.0360.0000.1280.1120.999
partyid_parlgov0.0240.0540.0340.3030.3020.0820.1750.0990.0430.0740.0370.303-0.2430.0090.718-0.2611.0000.7590.024-0.0060.2580.0400.3600.1860.3570.0400.0460.0000.0310.0990.1090.998
partyid_parlgov2-0.276-0.302-0.2700.5410.5390.3010.4740.205-0.1410.1750.2350.338-0.1960.1730.7510.0380.7591.000-0.276-0.0270.2110.0640.3020.1610.3100.0650.0400.0380.0260.0410.0371.000
id_de_parliament_string1.0000.6420.6230.1150.1140.077-0.416-0.0130.065-0.054-0.180-0.060-0.146-0.258-0.1120.1260.024-0.2761.0000.6200.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0001.000
mp_id_old0.6200.7590.7590.0650.0640.141-0.236-0.1060.197-0.020-0.262-0.031-0.160-0.253-0.1680.082-0.006-0.0270.6201.0000.3100.0390.1350.1430.2690.0830.0650.0450.0730.0250.0510.110
gender0.0000.2890.3160.0420.0260.0750.0000.0510.0540.1240.1450.2090.2060.0900.2720.2700.2580.2110.0000.3101.0000.0150.1800.1800.1070.0260.0000.0220.0270.0200.0490.254
office_spell0.0000.0690.0230.0430.0360.0000.0000.0000.0000.0580.0190.0140.0310.0330.0370.0250.0400.0640.0000.0390.0151.0000.0290.0320.0470.1320.1920.0700.2170.1590.1250.000
mandate0.0000.0560.0520.0960.1030.0080.0000.0620.1200.2230.1660.9850.5370.2930.4280.5560.3600.3020.0000.1350.1800.0291.0001.0000.1610.0550.0410.0180.0430.0000.0320.463
mandate_detailed0.0000.2200.0720.0700.0730.0140.0000.0360.0740.1660.0980.6980.4500.4960.2200.3980.1860.1610.0000.1430.1800.0321.0001.0000.2920.0620.0590.0250.0760.0520.0450.335
dualcand0.0090.2720.2370.0260.0250.1800.0000.2750.2240.1150.1560.3580.5580.1420.3630.3970.3570.3100.0090.2690.1070.0470.1610.2921.0000.0440.0460.0310.0000.0580.0330.329
minister0.0000.0510.0700.0000.0180.0360.0000.0450.0400.1230.0700.0580.1110.0950.0810.0600.0400.0650.0000.0830.0260.1320.0550.0620.0441.0000.0330.0130.0420.0310.0260.110
junminister0.0000.1000.1030.0300.0210.0220.0000.0040.0160.0610.0510.0540.0720.0900.0480.0830.0460.0400.0000.0650.0000.1920.0410.0590.0460.0331.0000.0160.0460.0440.0210.200
parlpres0.0000.0000.0630.0000.0000.0300.0000.0000.0060.0690.0370.0230.0610.0540.0270.0360.0000.0380.0000.0450.0220.0700.0180.0250.0310.0130.0161.0000.0430.1720.0120.000
commchair0.0000.0860.1120.0000.0000.0320.0000.0410.0350.0420.0210.0300.0530.0850.0190.0000.0310.0260.0000.0730.0270.2170.0430.0760.0000.0420.0460.0431.0000.0000.0130.000
ppgchair0.0000.0400.0320.0450.0110.0240.0000.0000.0230.1490.0410.0140.0890.0700.1270.1280.0990.0410.0000.0250.0200.1590.0000.0520.0580.0310.0440.1720.0001.0000.0390.017
whip0.0000.0320.0600.0270.0140.0330.0000.0000.0600.0790.0460.0480.0440.0160.1150.1120.1090.0370.0000.0510.0490.1250.0320.0450.0330.0260.0210.0120.0130.0391.0000.050
partyid_bl1.0001.0000.0871.0001.0000.0311.0000.0000.0000.1720.3060.3170.1020.2041.0000.9990.9981.0001.0000.1100.2540.0000.4630.3350.3290.1100.2000.0000.0000.0170.0501.000

Missing values

2023-12-03T11:21:57.399079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-03T11:21:57.957202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-03T11:21:58.376093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_de_parliamentlastnamefirstnameelecpergenderyear_birthdate_birthdate_birth_textmandate_startmandate_endoffice_spellspell_startspell_endparty_elecparty_elecdetmandatemandate_detaileddualcanddistrict_iddistrictvotecloseness_districtlistlistposlistpos_totalelecsafe_districtelecsafe_listelecsafe_overallministerjunministerparlprescommchairppgchairwhippartyid_cmppartyid_chespartyid_blpartyid_parlgovpartyid_parlgov2id_de_parliament_stringmp_id_oldpers_id_pdbdid_de_manow
011000001.0AbeleinManfred8119301930-10-2020/10/19301976-12-141980-11-041.01976-12-141980-11-0422.0110174.00.5710.202897NaNNaNNaN0.9824700.0000000.98247000000041521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
111000001.0AbeleinManfred7119301930-10-2020/10/19301972-12-131976-12-131.01972-12-131976-12-1322.0110174.00.5310.114864NaNNaNNaN0.9495560.0000000.94955600000041521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
211000001.0AbeleinManfred11119301930-10-2020/10/19301987-02-181990-12-201.01987-02-181990-12-2022.0110174.00.5290.184236NaNNaNNaN0.9913590.0000000.99135900000041521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
311000001.0AbeleinManfred6119301930-10-2020/10/19301969-10-201972-09-221.01969-10-201972-09-2222.0110174.00.5670.224192NaNNaNNaN0.9803230.0000000.98032300000041521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
411000001.0AbeleinManfred9119301930-10-2020/10/19301980-11-041983-03-291.01980-11-041983-03-2922.0110174.00.5350.154658NaNNaNNaN0.9635920.0000000.96359200000041521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
511000001.0AbeleinManfred5119301930-10-2020/10/19301965-10-191969-10-191.01965-10-191969-10-1922.0110174.00.5770.276113NaNNaNNaN0.9912720.0000000.99127200000041521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
611000001.0AbeleinManfred10119301930-10-2020/10/19301983-03-291987-02-171.01983-03-291987-02-1722.0110174.00.5850.241266NaNNaNNaN0.9917840.0000000.99178400000041521.0NaNNaN1727.0808.01100000110.0DE_Abelein_Manfred_193010
711000002.0AchenbachErnst5119091909-04-0909/04/19091965-10-191969-10-191.01965-10-191969-10-1944.002197.0NaN0.1912581.07.045.00.0004570.9947210.99472300000041420.0NaNNaN543.0NaN1100000214.0DE_Achenbach_Ernst_190914
811000002.0AchenbachErnst4119091909-04-0909/04/19091961-10-171965-10-171.01961-10-171965-10-1744.002199.0NaN0.0888741.06.061.00.0038200.9899410.98997900000041420.0NaNNaN543.0NaN1100000214.0DE_Achenbach_Ernst_190914
911000002.0AchenbachErnst6119091909-04-0909/04/19091969-10-201972-09-222.01971-12-071972-09-2244.002197.0NaN0.2451911.09.056.00.0002120.9160570.91607500001041420.0NaNNaN543.0NaN1100000214.0DE_Achenbach_Ernst_190914
id_de_parliamentlastnamefirstnameelecpergenderyear_birthdate_birthdate_birth_textmandate_startmandate_endoffice_spellspell_startspell_endparty_elecparty_elecdetmandatemandate_detaileddualcanddistrict_iddistrictvotecloseness_districtlistlistposlistpos_totalelecsafe_districtelecsafe_listelecsafe_overallministerjunministerparlprescommchairppgchairwhippartyid_cmppartyid_chespartyid_blpartyid_parlgovpartyid_parlgov2id_de_parliament_stringmp_id_oldpers_id_pdbdid_de_manow
1296311004962.0DahmenJanosch19119811981-09-0606/09/19812020-11-122021-10-261.02020-11-122021-10-2655.0041139.00.0858640.2812631.014.039.00.0007430.3427780.34326600000041113.0304.0NaN772.0772.011004962NaNNaNNaN
1296411004966.0NordtKristina19019821982-02-1717/02/19822021-03-222021-10-261.02021-03-222021-10-2622.0040NaNNaNNaN12.06.013.00.0000000.0002720.00027200000041521.0301.0NaN1727.0808.011004966NaNNaNNaN
1296511004967.0Friemann-JennertMaika19019641964-06-2424/06/19642021-04-072021-10-261.02021-04-072021-10-2622.0040NaNNaNNaN14.07.011.00.0000000.0000850.00008500000041521.0301.0NaN1727.0808.011004967NaNNaNNaN
1296611004968.0GohlChristopher19119741974-05-2323/05/19742021-05-012021-10-261.02021-05-012021-10-2644.0041290.00.0790460.2777873.013.037.00.0003470.2610550.26131100000041420.0303.0NaN543.0543.011004968NaNNaNNaN
1296711004969.0EmmerichMarcel19119911991-05-1212/05/19912021-06-012021-10-261.02021-06-012021-10-2655.0041291.00.1196390.3069873.016.040.00.0001630.0158960.01605600000041113.0304.0NaN772.0772.011004969NaNNaNNaN
1296811004970.0SusanneWetterich19019561956-04-2121/04/19562021-07-012021-10-261.02021-07-012021-10-2622.0040NaNNaNNaN3.011.060.00.0000000.1963960.19639600000041521.0301.0NaN1727.0808.011004970NaNNaNNaN
1296911004971.0FlorianJ��ger19119711971-01-1818/01/19712021-07-202021-10-261.02021-07-202021-10-261919.0041215.00.1022290.3341262.015.0NaN0.0000400.1424620.14249600000041953.0310.0NaN2253.02253.011004971NaNNaNNaN
1297011004972.0ZekiG��khan19119561956-02-1212/02/19562021-08-192021-10-261.02021-08-192021-10-2666.004191.00.0453680.3465581.014.030.00.0001530.0460130.04615900000041222.0306.0NaN791.0791.011004972NaNNaNNaN
1297155555556.0HinzPeter18119581958-04-1010/04/19582013-10-272013-10-221.02013-10-272013-10-2222.0020NaNNaNNaN3.016.055.00.0000000.1716480.17164800000041521.0301.0NaN1727.0808.055555556NaNNaNNaN
1297266666664.0BerndBuchholz19119611961-11-0202/11/19612017-10-242017-10-241.02017-10-242017-10-2444.002110.00.0822620.3126859.02.010.00.0000960.7670550.76707800000041420.0303.0NaN543.0543.066666664NaNNaNNaN